# TypeSafe AI's "Meaningful Intelligence"

> Source: <https://docs.typesafe.ai/introduction/machine-learning-primer>
> Published: 2026-09-18 10:40:19+00:00

**We call this Machine Native Intelligence:**
AI with software-like properties such as structure, reliability, observability, testability, speed, consistency, and low cost.

## Building prod, not God

TypeSafe is not trying to build a model that does everything. It is designed for production systems where code needs a narrow decision it can inspect and act on. Our expectation is that large-scale AI automation will be closer to 99% machine-to-machine interactions and 1% human interaction. That shifts the design target from responses that feel good to read toward outputs that behave predictably inside software. Read the
[TypeSafe manifesto](https://typesafe.ai/manifesto).

## Three post-training approaches

Pretrained language models have been adapted in two major ways. TypeSafe adds a third. RLHF and RLVR are shown here for context; TypeSafe’s training path is RLCD.
RLHF was used to train InstructGPT and ChatGPT and was 

[co-invented by Diogo Almeida](https://scholar.google.com/citations?user=0T4y07QAAAAJ&hl=en), cofounder of TypeSafe.

## RLCD and calibrated decisions

RLCD optimizes for a different output contract:
- The model does not generate text.
- It returns decisions and probabilities.
- Higher probability should correspond to a greater chance that the answer is correct.

- Outcomes assigned a probability of `0.2` should occur about 20% of the time.
- Outcomes assigned a probability of `0.8` should occur about 80% of the time.
- Outcomes assigned a probability of `1.0` should occur 100% of the time.

[Confidence](https://docs.typesafe.ai/confidence)for guidance on deciding when software should act or escalate.

## The problems with RLHF

RLHF teaches a model to say things that people prefer. That objective works well for chatbots, but it can also reward sycophancy and confident-sounding hallucinations. Preference optimization also causes
**mode dropping**: the model learns to favor a particular style, such as instruction following, while reducing the probability of other possible outputs.

**mode collapse**. In the classic generative-adversarial-network failure mode, a generator learns to produce the same kind of output repeatedly because that output continues to fool the discriminator.

## Mode collapse analogy

Mode collapse analogy